zhihebi

一个对几幅图象进行盲信号分离的MATLAB程序。 分离性能还不错。(The pieces of an image for Blind Signal Separation of the MATLAB program. Separation performance is not bad.)

it is the program for echo cancellor using the rls algorithm

Matlab图像处理实例详解第四章源码，欢迎大家下载学习，很好很强哟！！(Detailed examples Matlab image processing source code chapter are welcome to download the study, very strong yo! !)

matlab遗传算法求解tsp问题源程序(matlab genetic algorithm tsp problem source)

基于分段非线性权重值的Pso算法，GPS和INS组合导航程序，比较了软阈值，硬阈值及当今各种阈值计算方法。( Based on piecewise nonlinear weight value Pso algorithm, GPS and INS navigation program, Comparison of soft threshold and hard threshold and today various threshold calculation method.)

说明：  信号分析与处理的一些源代码，有助于初学者快速掌握学习信号处理方法。(signal analysis and processing of source code, help beginners grasp the study signal processing methods.)

讲解最常用的matlab算法，用自己深刻的体会加以整理(Matlab explain the most commonly used algorithms, deep understanding of their own to organize)

简单的实例，介绍了基本的用法，适合有一定基础的学者，可以巩固自己的知识(Simple example, describes the basic usage, there is some basis for scholars, can consolidate their knowledge)

Fuzzy c-means clustering (FCM) with spatial constraints (FCM_S) is an effective algorithm suitable for image segmentation. Its effectiveness contributes not only to introduction of fuzziness for belongingness of each pixel but also to exploitation of spatial contextual information. Although the contextual information can raise its insensitivity to noise to some extent, FCM_S (1) still lacks enough robustness to noise and outliers and (2) is not suitable for revealing non-Euclidean structure of the input data due to the use of Euclidean distance (L2 norm).

说明：  工程实际中的许多优化问题是多目标的优化设计问题。通常情况下多个目标是处于冲突状态的,不存在一个最优设计使所有的目标同时达到最优,一个目标性能的改善,往往以其它一个或多个目标性能的降低为代价。无论采用自适应遗传算法还是其它在标准遗传算法基础上经过改进的遗传算法,如果采用加权组合法等将多目标转化为单目标的处理方法,就无法充分发挥遗传算法的优势。如果把遗传 算法与适用于多目标处理的几此协方法相结合,就能完全发挥遗传算法在多目标优化设计中的作用。(Many optimization problems in engineering practice are multi-objective optimization design problems. Generally, multiple goals are in conflict. There is no one optimal design to achieve all goals simultaneously. The improvement of one goal's performance is often at the expense of the performance of one or more other goals. Whether adaptive genetic algorithm or other improved genetic algorithm based on standard genetic algorithm is adopted, if weighted combination method is adopted to transform multi-objective into single-objective processing method, the advantages of genetic algorithm cannot be fully utilized. If the genetic algorithm is combined with several cooperative methods suitable for multi-objective processing, the role of genetic algorithm in multi-objective optimal design can be fully exerted.)